Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	33.0	12.007868
daal4py_KNeighborsClassifier	0.0	6.0	22.106278
KNeighborsClassifier_kd_tree	0.0	2.0	37.627022
daal4py_KNeighborsClassifier_kd_tree	0.0	0.0	31.648181
KMeans_tall	0.0	0.0	26.798776
daal4py_KMeans_tall	0.0	0.0	10.952872
KMeans_short	0.0	0.0	3.723011
daal4py_KMeans_short	0.0	0.0	1.964744
LogisticRegression	0.0	0.0	26.822800
daal4py_LogisticRegression	0.0	0.0	5.988052
Ridge	0.0	0.0	11.874550
daal4py_Ridge	0.0	0.0	2.690213
HistGradientBoostingClassifier	0.0	5.0	0.593164
lightgbm	0.0	5.0	18.704248
xgboost	0.0	5.0	16.497829
catboost	0.0	5.0	41.355511
total	1.0	5.0	31.466991

KNeighborsClassifier vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	brute

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.595	0.000	1.345	0.000	1	100	NaN	NaN	0.568	0.000	1.047	0.000	See	See
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	24.949	0.325	0.000	0.025	1	100	0.927	0.954	4.561	0.068	5.471	0.108	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.228	0.011	0.000	0.228	1	100	1.000	1.000	0.108	0.007	2.118	0.168	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.137	0.000	5.828	0.000	-1	1	NaN	NaN	0.553	0.000	0.248	0.000	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	31.497	0.000	0.000	0.031	-1	1	0.711	0.954	4.582	0.042	6.874	0.063	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.204	0.025	0.000	0.204	-1	1	1.000	1.000	0.108	0.002	1.891	0.233	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.139	0.000	5.765	0.000	-1	5	NaN	NaN	0.567	0.000	0.245	0.000	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	38.166	0.000	0.000	0.038	-1	5	0.797	0.818	4.484	0.035	8.512	0.066	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.201	0.017	0.000	0.201	-1	5	1.000	1.000	0.104	0.002	1.944	0.163	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.145	0.000	5.534	0.000	1	1	NaN	NaN	0.546	0.000	0.265	0.000	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	18.141	0.163	0.000	0.018	1	1	0.711	0.719	4.480	0.024	4.049	0.043	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.224	0.007	0.000	0.224	1	1	1.000	1.000	0.106	0.004	2.109	0.102	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.141	0.000	5.659	0.000	-1	100	NaN	NaN	0.560	0.000	0.252	0.000	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	38.783	0.000	0.000	0.039	-1	100	0.927	0.818	4.504	0.030	8.610	0.057	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.201	0.025	0.000	0.201	-1	100	1.000	1.000	0.107	0.002	1.880	0.238	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.138	0.000	5.790	0.000	1	5	NaN	NaN	0.544	0.000	0.254	0.000	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	24.809	0.347	0.000	0.025	1	5	0.797	0.719	4.530	0.048	5.476	0.096	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.222	0.007	0.000	0.222	1	5	1.000	1.000	0.112	0.006	1.991	0.129	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.057	0.000	0.280	0.000	1	100	NaN	NaN	0.106	0.000	0.542	0.000	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	20.649	0.171	0.000	0.021	1	100	0.987	0.986	1.083	0.017	19.059	0.338	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.025	0.002	0.000	0.025	1	100	1.000	1.000	0.005	0.000	5.413	0.591	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.056	0.000	0.286	0.000	-1	1	NaN	NaN	0.098	0.000	0.567	0.000	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	25.027	0.246	0.000	0.025	-1	1	0.977	0.986	1.083	0.026	23.103	0.595	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.021	0.002	0.000	0.021	-1	1	1.000	1.000	0.005	0.000	4.507	0.544	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.053	0.000	0.304	0.000	-1	5	NaN	NaN	0.098	0.000	0.538	0.000	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	33.334	0.000	0.000	0.033	-1	5	0.986	0.985	1.008	0.013	33.074	0.442	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.030	0.002	0.000	0.030	-1	5	1.000	1.000	0.005	0.000	6.451	0.634	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.056	0.000	0.284	0.000	1	1	NaN	NaN	0.102	0.000	0.553	0.000	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	11.695	0.069	0.000	0.012	1	1	0.977	0.978	1.006	0.020	11.621	0.237	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.014	0.002	0.000	0.014	1	1	1.000	1.000	0.005	0.000	2.832	0.505	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.052	0.000	0.306	0.000	-1	100	NaN	NaN	0.096	0.000	0.546	0.000	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	33.788	0.000	0.000	0.034	-1	100	0.987	0.985	1.022	0.035	33.061	1.143	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.030	0.002	0.000	0.030	-1	100	1.000	1.000	0.005	0.000	6.619	0.699	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.049	0.000	0.324	0.000	1	5	NaN	NaN	0.103	0.000	0.481	0.000	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	20.312	0.094	0.000	0.020	1	5	0.986	0.978	1.001	0.011	20.300	0.244	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.025	0.001	0.000	0.025	1	5	1.000	1.000	0.005	0.000	5.209	0.550	See	See

KNeighborsClassifier_kd_tree vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	kd_tree

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.768	0.000	0.029	0.000	1	1	NaN	NaN	0.778	0.000	3.556	0.000	See	See
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.744	0.030	0.000	0.001	1	1	0.957	0.977	0.210	0.005	3.533	0.167	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	3.080	1.736	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.766	0.000	0.029	0.000	1	100	NaN	NaN	0.778	0.000	3.558	0.000	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	4.757	0.070	0.000	0.005	1	100	0.976	0.961	0.117	0.005	40.741	1.840	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.001	0.000	0.004	1	100	1.000	1.000	0.000	0.000	11.723	7.011	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.928	0.000	0.027	0.000	-1	1	NaN	NaN	0.755	0.000	3.876	0.000	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.414	0.006	0.000	0.000	-1	1	0.957	0.973	0.648	0.014	0.639	0.017	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.000	0.000	0.003	-1	1	1.000	1.000	0.001	0.000	4.795	1.788	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.774	0.000	0.029	0.000	1	5	NaN	NaN	0.770	0.000	3.603	0.000	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.405	0.036	0.000	0.001	1	5	0.973	0.961	0.116	0.008	12.072	0.913	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	3.732	1.926	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.769	0.000	0.029	0.000	-1	100	NaN	NaN	0.751	0.000	3.688	0.000	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	2.801	0.063	0.000	0.003	-1	100	0.976	0.977	0.209	0.007	13.397	0.547	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.006	0.001	0.000	0.006	-1	100	1.000	1.000	0.000	0.000	16.909	8.013	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.709	0.000	0.030	0.000	-1	5	NaN	NaN	0.738	0.000	3.672	0.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.851	0.021	0.000	0.001	-1	5	0.973	0.973	0.646	0.015	1.317	0.044	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.002	0.000	0.004	-1	5	1.000	1.000	0.001	0.000	6.981	3.877	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.721	0.000	0.022	0.000	1	1	NaN	NaN	0.523	0.000	1.379	0.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.029	0.002	0.001	0.000	1	1	0.982	0.981	0.001	0.000	21.556	5.554	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	4.563	3.800	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.736	0.000	0.022	0.000	1	100	NaN	NaN	0.525	0.000	1.403	0.000	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.058	0.005	0.000	0.000	1	100	0.984	0.975	0.001	0.000	63.016	22.917	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	4.473	3.081	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.721	0.000	0.022	0.000	-1	1	NaN	NaN	0.513	0.000	1.407	0.000	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.032	0.001	0.000	0.000	-1	1	0.982	0.979	0.009	0.001	3.603	0.501	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.001	0.000	0.003	-1	1	1.000	1.000	0.000	0.000	13.895	7.881	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.756	0.000	0.021	0.000	1	5	NaN	NaN	0.539	0.000	1.402	0.000	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.032	0.002	0.001	0.000	1	5	0.986	0.975	0.001	0.000	36.009	11.224	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	4.700	2.599	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.724	0.000	0.022	0.000	-1	100	NaN	NaN	0.526	0.000	1.376	0.000	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.052	0.002	0.000	0.000	-1	100	0.984	0.981	0.001	0.000	40.593	9.814	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.001	0.000	0.003	-1	100	1.000	1.000	0.000	0.000	12.321	7.080	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.734	0.000	0.022	0.000	-1	5	NaN	NaN	0.536	0.000	1.368	0.000	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.038	0.008	0.000	0.000	-1	5	0.986	0.979	0.008	0.001	4.812	1.096	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.000	0.000	0.003	-1	5	1.000	1.000	0.000	0.000	14.454	7.400	See	See

KMeans_tall vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.745	0.000	0.644	0.000	k-means++	NaN	30	NaN	0.320	0.0	2.328	0.000	See	See
1	KMeans_tall	predict	1000000	1000	2	30	0.002	0.000	0.281	0.000	k-means++	0.001	30	0.001	0.000	0.0	5.837	5.294	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.002	0.000	0.000	0.002	k-means++	1.000	30	1.000	0.000	0.0	9.552	5.018	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.633	0.000	0.758	0.000	random	NaN	30	NaN	0.337	0.0	1.876	0.000	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.002	0.000	0.276	0.000	random	0.001	30	0.001	0.000	0.0	6.919	3.544	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.002	0.000	0.000	0.002	random	1.000	30	1.000	0.000	0.0	9.046	3.296	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	8.619	0.000	2.784	0.000	k-means++	NaN	30	NaN	4.107	0.0	2.099	0.000	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.000	11.979	0.000	k-means++	0.002	30	0.002	0.000	0.0	5.115	2.746	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.002	0.001	0.012	0.002	k-means++	1.000	30	1.000	0.000	0.0	8.521	6.338	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	7.652	0.000	3.136	0.000	random	NaN	30	NaN	4.312	0.0	1.775	0.000	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.000	12.639	0.000	random	0.002	30	0.002	0.000	0.0	5.200	2.499	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.002	0.000	0.014	0.002	random	1.000	30	1.000	0.000	0.0	7.909	4.552	See	See

KMeans_short vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	20
n_init	1
tol	0.0

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	10000	10000	2	20	0.327	0.0	0.010	0.000	k-means++	NaN	20	NaN	0.157	0.0	2.079	0.000	See	See
1	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.146	0.000	k-means++	0.001	20	0.005	0.001	0.0	2.919	0.669	See	See
2	KMeans_short	predict	10000	1	2	20	0.002	0.0	0.000	0.002	k-means++	1.000	20	1.000	0.000	0.0	7.612	3.396	See	See
3	KMeans_short	fit	10000	10000	2	20	0.109	0.0	0.029	0.000	random	NaN	20	NaN	0.060	0.0	1.824	0.000	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.143	0.000	random	0.002	20	0.001	0.001	0.0	3.026	0.717	See	See
5	KMeans_short	predict	10000	1	2	20	0.002	0.0	0.000	0.002	random	1.000	20	1.000	0.000	0.0	9.232	3.143	See	See
6	KMeans_short	fit	10000	10000	100	20	1.190	0.0	0.134	0.000	k-means++	NaN	20	NaN	0.665	0.0	1.790	0.000	See	See
7	KMeans_short	predict	10000	1000	100	20	0.003	0.0	4.624	0.000	k-means++	0.332	20	0.364	0.002	0.0	2.128	0.311	See	See
8	KMeans_short	predict	10000	1	100	20	0.002	0.0	0.009	0.002	k-means++	1.000	20	1.000	0.000	0.0	7.855	4.040	See	See
9	KMeans_short	fit	10000	10000	100	20	0.387	0.0	0.414	0.000	random	NaN	20	NaN	0.275	0.0	1.406	0.000	See	See
10	KMeans_short	predict	10000	1000	100	20	0.003	0.0	4.759	0.000	random	0.290	20	0.308	0.002	0.0	2.101	0.231	See	See
11	KMeans_short	predict	10000	1	100	20	0.002	0.0	0.009	0.002	random	1.000	20	1.000	0.000	0.0	7.256	4.587	See	See

LogisticRegression vs daal4py

All estimators share the following hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	16.194	0.0	[-0.07285741]	0.000	NaN	NaN	NaN	NaN	NaN	3.139	0.0	5.159	0.000	See	See
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[36.51824965]	0.000	NaN	NaN	NaN	NaN	0.496	0.000	0.0	0.895	0.259	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.1426161]	0.000	NaN	NaN	NaN	NaN	1.000	0.000	0.0	0.384	0.297	See	See
3	LogisticRegression	fit	1000	1000	10000	[26]	1.433	0.0	[1.45180845]	0.001	NaN	NaN	NaN	NaN	NaN	1.160	0.0	1.235	0.000	See	See
4	LogisticRegression	predict	1000	100	10000	[26]	0.003	0.0	[82.69971963]	0.000	NaN	NaN	NaN	NaN	0.280	0.004	0.0	0.622	0.091	See	See
5	LogisticRegression	predict	1000	1	10000	[26]	0.000	0.0	[11.80015976]	0.000	NaN	NaN	NaN	NaN	0.000	0.001	0.0	0.180	0.132	See	See

Ridge vs daal4py

All estimators share the following hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	deprecated
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.330	0.000	0.242	0.0	NaN	NaN	NaN	0.342	0.000	0.965	0.000	See	See
1	Ridge	predict	1000	1000	10000	NaN	0.012	0.001	6.486	0.0	NaN	NaN	0.097	0.022	0.001	0.559	0.045	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.000	0.759	0.0	NaN	NaN	NaN	0.000	0.000	0.573	0.323	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.705	0.000	0.469	0.0	NaN	NaN	NaN	0.430	0.000	3.969	0.000	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.000	3.285	0.0	NaN	NaN	1.000	0.000	0.000	0.779	0.494	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.000	0.007	0.0	NaN	NaN	NaN	0.000	0.000	0.737	0.523	See	See

Environment information

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.3",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}